Spatio-temporal monitoring of health facility-level malaria trends in Zambia and adaptive scaling for operational intervention

Background The spatial and temporal variability inherent in malaria transmission within countries implies that targeted interventions for malaria control in high-burden settings and subnational elimination are a practical necessity. Identifying the spatio-temporal incidence, risk, and trends at different administrative geographies within malaria-endemic countries and monitoring them in near real-time as change occurs is crucial for developing and introducing cost-effective, subnational control and elimination intervention strategies. Methods This study developed intelligent data analytics incorporating Bayesian trend and spatio-temporal Integrated Laplace Approximation models to analyse high-burden over 32 million reported malaria cases from 1743 health facilities in Zambia between 2009 and 2015. Results The results show that at least 5.4 million people live in catchment areas with increasing trends of malaria, covering over 47% of all health facilities, while 5.7 million people live in areas with a declining trend (95% CI), covering 27% of health facilities. A two-scale spatio-temporal trend comparison identified significant differences between health facilities and higher-level districts, and the pattern observed in the southeastern region of Zambia provides the first evidence of the impact of recently implemented localised interventions. Conclusions The results support our recommendation for an adaptive scaling approach when implementing national malaria monitoring, control and elimination strategies and a particular need for stratified subnational approaches targeting high-burden regions with increasing disease trends. Strong clusters along borders with highly endemic countries in the north and south of Zambia underscore the need for coordinated cross-border malaria initiatives and strategies.

During the period of this study from 2009 to 2015, the number of health facilities reporting in the health management information system (HMIS) increased from 1552 in 2009 to about 1869 in 2015. The increase was made up of a combination of government, mission (faithbased), and private providers [1][2][3][4]. During the same period, Zambia's population increased by 18.5%, while the total number of health facilities increased by 18.7%, and the ratio of health facilities to population ratio remained the same throughout (1.2 health facilities per 10 000 population). Based on a comparison between population growth and the increase in health facilities, it is proposed that the construction of new health facilities was primarily driven by population growth and, as such, can be argued that given the population growth ratio was consistent with the provision of health care facilities per person, it could logically be assumed that both incidence rates and trends of malaria should have remained constant unless other factors were at play. Such factors could include -climate variability (favouring malaria infections), the geographical variance of sub-district malaria intervention distribution, or associated differences in cultural attitudes towards the utilisation of malaria interventions, all of which could influence the effectiveness of interventions against malaria.
In addition, 76 health facilities were excluded due to incomplete malaria data, the lack of both malaria and population data, or more specifically, the absence of a reliable population denominator. Malaria cases reported at these 76 health facilities accounted for 0.8% of the total reported malaria between 2009 and 2015 available for analysis. No imputation methods were applied because information on missingness was not available.
The dataset was divided into two sets, with a separation between 1743 low-level health facilities from 100 higher-level hospitals. We grouped health posts, health centres, and clinics, together with all private health facilities into the first group for analysis of lower-level (primary care) health facilities where most of the malaria outpatient department (OPD) screening and treatment happens. The other set comprised only public level 3 and referral (secondary/tertiary care) hospitals where malaria cases treated are the most severe and often involve admissions. Given that most of the severe malaria cases received at hospitals are referrals from primary health facilities within hospital catchments or the Hospital Affiliated Health Centres (HAHC) [5]. HAHCs act as onsite OPDs for the hospitals, which meant that including referral hospitals would more likely lead to double counting of cases already received at the health centre level before the referral rather than missing cases out. This separation helped us avoid double-counting malaria cases referred to hospitals from lower-level health facilities. All private hospitals were included in the first group because they also provide services offered by lower-level public health facilities.

The implication of the changing number of health facilities
Due to the periodic construction of new health facilities and upgrading of other lower facilities to higher-level functions, the number of health facilities with at least one case reported in the 2 HMIS increased by about 600 between 2012 and 2016. While this increase in newly constructed health facilities captured in the reporting system may be interpreted as a possible explanation for why there may be increases in malaria cases, based on the point made above regarding the virtually identical corresponding increase in population, we would argue that in fact, this has little to do with observed increases in malaria, and instead is simply a reflection of enhanced access, and possible improvements to the quality of health services provided. This is particularly true for urban areas where more people tend to seek health facility services relative to the planned/expected facility capacity or in rural areas where some villages are far from the available rural health facilities.
Nonetheless, most new centres still take a long time to gain the trust of people around them as most patients would still opt for the facility they have previous experience with even if this is further away [6]. Such is often observed in people's frequent preference for hospitals over health facilities even though the health centre nearer to their location is adequately equipped to handle the same disease. We would propose that while the changing number of health facilities improves the quality of health services offered, it does not affect the actual long-term district or health facility trend reporting but it may temporarily affect time point-specific records at surrounding health facilities.

Low Spatial (intra-district) district-level and temporal (annual) trend variation
The mean posterior exceedance probabilities of health facilities in more northern areas are higher (Supplementary Figure 6) than those in the southern region. The same is true with posterior median estimates (Supplementary Figure 7) which show a similar spatial transitional pattern generally higher in the north compared to the southern areas. The overall model in INLA, with type II interaction, was the best model compared to Type I, III, and IV. It had a marginal log-Likelihood of -103348.76, and the linear predictor and fitted posterior marginal decomposition of the total amount of variability from log rates was 68.05% 3.32%, and 28.63% for Spatial, Temporal, and Space-time, respectively. This model confirms little temporal variance, while the bulk of the variance could be explained by the spatial and the spatiotemporal components. The details of the posterior marginal distributions using precision and variance scales are given in Supplementary Tables 3 and Supplementary 4